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Computer Science > Machine Learning

Title: RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management

Abstract: In recent years, digital platform companies have faced increasing challenges in managing customer complaints, driven by widespread consumer adoption. This paper introduces an end-to-end pipeline, named RE-GrievanceAssist, designed specifically for real estate customer complaint management. The pipeline consists of three key components: i) response/no-response ML model using TF-IDF vectorization and XGBoost classifier ; ii) user type classifier using fasttext classifier; iii) issue/sub-issue classifier using TF-IDF vectorization and XGBoost classifier. Finally, it has been deployed as a batch job in Databricks, resulting in a remarkable 40% reduction in overall manual effort with monthly cost reduction of Rs 1,50,000 since August 2023.
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2404.18963 [cs.LG]
  (or arXiv:2404.18963v1 [cs.LG] for this version)

Submission history

From: Anil Goyal [view email]
[v1] Mon, 29 Apr 2024 07:03:23 GMT (3254kb,D)

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